User Behavior Segmentation Using Latent Topic Detection

1. A method of artificial intelligence guided segmentation of event data, the method comprising: accessing, from a data store, a plurality of event records associated with respective users of a plurality of users, wherein a first plurality of event records associated with a first user are stored using a first quantity of storage; accessing an event categories data structure indicating a plurality of event categories and, for each event category, attribute criteria usable to identify events associated with respective event categories; for the event records, identifying one or more attributes of the event record, comparing the identified one or more attributes of the event record to the attribute criteria of respective event categories, and based on said comparing, assigning, to the event record, an event category having attribute criteria matching the identified one or more attributes of the event record; generating, for the first user, first compressed event data using the event records associated with the first user and a latent feature identification model, wherein the latent feature identification model takes the event records for the first user and the event categories assigned thereto as an input, and provides association values for the first user for respective event topics identified by the first compressed event data, wherein first compressed event data associated with the first user is stored using a second quantity of storage, the second quantity of storage being less than the first quantity of storage for storing the event records of the first user; assigning the first user to one of a plurality of data clusters included in a clustering model using the first compressed event data for the first user; and generating, for the first user, second compressed event data using a comparison between the first compressed event data for the first user and an average latent feature identification value for a latent feature included in the data cluster to which the first user has been assigned, wherein the second compressed event data associated with the first user is stored using a third quantity of storage, the third quantity of storage being less than the second quantity of storage.

2. The method of claim 1 , wherein assigning the first user to one of the data clusters comprises: identifying center points for each data cluster included in the clustering model; generating an association strength for each latent feature included in the first compressed event data for the first user for each data cluster, the association strength indicating a degree of association between the first compressed event data for a user and respective data cluster center points; and identifying the one of the data clusters as having the highest association strength for the first user from amongst the data clusters included in the clustering model.

3. The method of claim 2 , wherein generating the association strength for the first user comprises comparing a latent feature identification value included in the first compressed event record for a latent feature for the first user to the center point.

4. The method of claim 2 , wherein generating the second compressed event data further comprises: generating a secondary association strength for each latent feature included in the first compressed event data for a user assigned to the data cluster, the secondary association strength indicating a secondary degree of association between the first compressed event data for the user assigned to the data cluster and the secondary center point of the secondary data cluster to which the user is not assigned, wherein the second compressed event data comprises an identifier for the secondary data cluster and the generated secondary association strengths.

5. The method of claim 2 , further comprising: accessing content data including a content identifier and an indication of a target data cluster of the data clusters; identifying a plurality of users assigned to the target data cluster; selecting a target set of users having second compressed event data including generated association strengths indicating a threshold degree of association to the center point of the target data cluster; and generating an electronic communication to provide to the target set of user profiles, the electronic communication including content indicated by the content identifier.

6. The method of claim 1 , further comprising: training the latent feature identification model through probabilistic analysis of a plurality of historical event records to identify a target number of topics; and training the clustering model using a desired compression level indicating a number of data clusters for the clustering model, wherein training the clustering model includes generating a center point for each data cluster using topically compressed historical event data.

7. The method of claim 1 , wherein the latent feature identification model comprises a latent dirichlet allocation model.

8. A method of compressing transaction data, the method comprising: receiving a plurality of transaction records each identifying a transaction by one of a plurality of users; assigning a category to each of the plurality of transaction records; generating first compressed transaction records using a latent feature identification model, wherein the latent feature identification model takes the transaction records for the one of the plurality of users and categories assigned thereto as an input, and provides association values for the one of the plurality of users for respective topics identified in the first compressed event data; identifying a clustering compression model for the one of the plurality of users; and generating second compressed transaction records using the first compressed transaction records and the clustering compression model.

9. The method of claim 8 , wherein generating a first compressed transaction record for the one of the plurality of users comprises receiving association strengths for each topic identified by the latent feature identification model for a set of transactions for the one of the plurality of users.

10. The method of claim 8 , further comprising: receiving a compression configuration indicating a target number of features to identify for an end user; and training a latent dirichlet allocation model to identify the target number of features using the received plurality of transaction records, wherein the latent feature identification model comprises the latent dirichlet allocation model.

11. The method of claim 8 , wherein each data cluster included in the clustering compression model is associated with at least one latent feature identifiable by the latent feature identification model, and wherein generating the second compressed transaction records comprises: assigning each user to one of the data clusters using the first compressed transaction records; and generating the second compressed transaction records for each user using a comparison between the first compressed transaction data for a user and the center point for the cluster to which the user is assigned.

12. The method of claim 11 , where generating the second compressed transaction records further comprises: calculating a secondary center point for a secondary data cluster using first compressed transaction data for each user assigned to the secondary data cluster; and generating a secondary association strength for each latent feature included in the first compressed transaction data for a user assigned to the data cluster, the secondary association strength indicating a secondary degree of association between the first compressed transaction data for the user assigned to the data cluster and the secondary center point of the secondary data cluster to which the user is not assigned, wherein the second compressed transaction data comprises an identifier for the secondary data cluster and the generated secondary association strengths.

13. The method of claim 8 , further comprising training a clustering model using the desired compression level and at least a portion of the plurality of transaction records.

14. The method of claim 8 , further comprising: receiving, from a transaction terminal, a pending transaction record for a user included in the plurality of users, wherein the pending transaction record is not included in the plurality of transaction records; retrieving a second compressed transaction record for the user using an identifier of the user included in the pending transaction record; and transmitting the second compressed transaction record to the transaction terminal.

15. The method of claim 14 , further comprising: selecting a content element for presentation to the user during or after the pending transaction using the second compressed transaction record; and providing the content element to a content delivery system configured to transmit the content element to the user.

16. A transaction data compression system comprising: one or more computer processors configured to execute software instructions; a non-transitory tangible storage device storing the software instructions executable by the one or more processors to at least: access transaction data associated with a plurality of users; for a plurality of transactions in the transaction data: assign a transaction category based on one or more attributes of the transaction; and assign a transaction category level for the transaction category based at least in part on on spend levels of individual users associated with the plurality of transactions; and generate, for each user, first compressed transaction data using the transaction categories assigned to the transaction records for a respective user and a latent feature identification model, wherein the latent feature identification model takes the event records for the first user and the event categories assigned thereto as an input, and provides association values for the first user for respective event topics identified by the first compressed event data, wherein the first compressed transaction data associated with the one of the respective users is stored using a second quantity of storage, the second quantity of storage being less than the first quantity of storage; identify a clustering compression model for users included in the plurality of users; assign each of the users to one of a plurality of data clusters included in the respective clustering compression model using respective first compressed transaction data for the user; and generate, for each user, second compressed transaction data using a comparison between the first compressed transaction data for a user and an average for the data cluster to which the user has been assigned, wherein the second compressed transaction data is stored using a third quantity of storage, the third quantity of storage being less than the second quantity of storage.

17. The transaction data compression system of claim 16 , wherein the software instructions are further executable by the one or more processors to at least: access content data including a content identifier and an indication of a target data cluster of the data clusters; identify a plurality of users assigned to the target data cluster; select a target set of users having second compressed transaction data including generated association strengths indicating a threshold degree of association to the center point of the target data cluster; and generate an electronic communication to provide to the target set of user profiles, the electronic communication including content indicated by the content identifier.

18. The transaction data compression system of claim 17 , wherein the software instructions are further executable by the one or more processors to at least: access the target set of users; identify a target device for a user included in the target set of users; and provide the electronic communication to the target device.

19. The transaction data compression system of claim 16 , further comprising a card reader including: one or more computer processors configured to execute software instructions; a non-transitory tangible storage device storing the software instructions executable by the one or more computer processors to cause the card reader to at least: detect payment information for a transaction for a user; receive compressed transaction data during the transaction for the user; and identify content stored by the card reader using a comparison between a content selection rule and the compressed transaction data, said content for presentation via the card reader; and a display configured to present the content to the user.

20. The transaction data compression system of claim 16 , wherein the software instructions are further executable by the one or more processors to at least generate at least one of the latent feature identification model and a clustering model identifying the plurality of data clusters for the plurality of transaction records.